{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result Viewer\n",
    "The scripts used to view the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Results\n",
    "Results for RQ1 (Comparison Experiments) and RQ2 (Ablation Study)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "result_dir = 'result'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_result(prompt: str, model: str) -> str:\n",
    "    result_dict = {\"TP\": 0, \"TN\": 0}\n",
    "    risky_results = json.load(open(os.path.join(result_dir,model,prompt, \"risky_result.json\")))\n",
    "    for r in risky_results:\n",
    "        if r[\"result\"] == \"sound\":\n",
    "            result_dict[\"TN\"] += 1\n",
    "    unsafe_results = json.load(open(os.path.join(result_dir,model,prompt, \"filtered_unsafe_result.json\")))\n",
    "    for r in unsafe_results:\n",
    "        if r[\"result\"] == \"unsound\" or r[\"result\"] == \"unknown\":\n",
    "            result_dict[\"TP\"] += 1\n",
    "    print(f\"{prompt}: \", result_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basic_check:  {'TP': 85, 'TN': 23}\n",
      "Safe4U:  {'TP': 76, 'TN': 146}\n"
     ]
    }
   ],
   "source": [
    "get_result(\"basic_check\", \"Meta-Llama-3.1-8B-Instruct\")\n",
    "get_result(\"Safe4U\", \"Meta-Llama-3.1-8B-Instruct\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basic_check:  {'TP': 61, 'TN': 74}\n",
      "Safe4U:  {'TP': 90, 'TN': 129}\n"
     ]
    }
   ],
   "source": [
    "get_result(\"basic_check\", \"Qwen2.5-Coder-7B-Instruct\")\n",
    "get_result(\"Safe4U\", \"Qwen2.5-Coder-7B-Instruct\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basic_check:  {'TP': 51, 'TN': 124}\n",
      "basic+CoT:  {'TP': 18, 'TN': 177}\n",
      "Safe4U-CoT:  {'TP': 55, 'TN': 139}\n",
      "Safe4U-hints:  {'TP': 63, 'TN': 171}\n",
      "Safe4U-references:  {'TP': 46, 'TN': 198}\n",
      "Safe4U-decompose:  {'TP': 11, 'TN': 213}\n",
      "Safe4U_using_embedding:  {'TP': 46, 'TN': 207}\n",
      "Safe4U-one_pattern_a_time:  {'TP': 42, 'TN': 200}\n",
      "Safe4U-self_check:  {'TP': 56, 'TN': 205}\n",
      "Safe4U_decompose_then_classify:  {'TP': 46, 'TN': 192}\n",
      "Safe4U:  {'TP': 56, 'TN': 205}\n"
     ]
    }
   ],
   "source": [
    "get_result(\"basic_check\", \"Qwen2-7B-Instruct\")\n",
    "get_result(\"basic+CoT\", \"Qwen2-7B-Instruct\")\n",
    "get_result(\"Safe4U-CoT\", \"Qwen2-7B-Instruct\")\n",
    "get_result(\"Safe4U-hints\", \"Qwen2-7B-Instruct\")\n",
    "get_result(\"Safe4U-references\", \"Qwen2-7B-Instruct\")\n",
    "get_result(\"Safe4U-decompose\", \"Qwen2-7B-Instruct\")\n",
    "# get_result(\"Safe4U-classified_contract\", \"Qwen2-7B-Instruct\")\n",
    "get_result(\"Safe4U_using_embedding\", \"Qwen2-7B-Instruct\")\n",
    "get_result(\"Safe4U-one_pattern_a_time\", \"Qwen2-7B-Instruct\")\n",
    "get_result(\"Safe4U-self_check\", \"Qwen2-7B-Instruct\")\n",
    "get_result(\"Safe4U_decompose_then_classify\", \"Qwen2-7B-Instruct\")\n",
    "get_result(\"Safe4U\", \"Qwen2-7B-Instruct\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-grained Analysis\n",
    "The effectiveness of fine-grained results in locating unsoundness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from utils.sample_utils import Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ground_truth_file = \"data/manual/filtered_unsafe_Qwen_GT.json\"\n",
    "prompt = \"Safe4U\"\n",
    "prompt = \"Safe4U\"\n",
    "model = \"Qwen2-7B-Instruct\"\n",
    "skip_hallucated = True\n",
    "result_file = f\"result/{model}/{prompt}/filtered_unsafe_result.json\"\n",
    "gt_list = json.load(open(ground_truth_file))\n",
    "pred_list = json.load(open(result_file))\n",
    "sample_dict = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_dict = {}\n",
    "for sample in gt_list:\n",
    "    gt_dict[sample[\"sample_label\"]] = sample[\"constraints\"]\n",
    "    sample_dict[sample[\"sample_label\"]] = Sample(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### contract-level recall & precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt = pd.DataFrame(np.zeros((2, 3)), columns=[\"No\", \"Yes\", \"unknown\"], index=[\"expect_No\", \"expect_Yes\"])\n",
    "unsound_cnt = 0\n",
    "hallucinated_cnt = 0\n",
    "for r in pred_list:\n",
    "    sample_label = r[\"sample_label\"]\n",
    "    if r[\"result\"] != \"unsound\":\n",
    "        continue\n",
    "    unsound_cnt += 1\n",
    "    if sample_label not in gt_dict:\n",
    "        print(f\"Sample {sample_label} not found in ground truth\")\n",
    "        continue\n",
    "    gt = gt_dict[sample_label]\n",
    "    for c_idx, c in enumerate(r[\"response\"]):\n",
    "        expect = gt[c_idx]['expect']\n",
    "        if expect == \"Hallucinated\":\n",
    "            hallucinated_cnt += 1\n",
    "            if skip_hallucated:\n",
    "                continue\n",
    "            else:\n",
    "                expect = \"Yes\"\n",
    "        cnt.loc[f\"expect_{expect}\", c[\"result\"]] += 1\n",
    "\n",
    "# add unknown to No\n",
    "cnt.loc[\"expect_No\", \"No\"] += cnt.loc[\"expect_No\", \"unknown\"]\n",
    "cnt.loc[\"expect_Yes\", \"No\"] += cnt.loc[\"expect_Yes\", \"unknown\"]\n",
    "\n",
    "print(cnt)\n",
    "print(f\"Hallucination: {hallucinated_cnt}\")\n",
    "print(f\"Unsound: {unsound_cnt}\")\n",
    "print(f\"Recall: {cnt.loc['expect_No', 'No'] / (cnt.loc['expect_No', 'Yes'] + cnt.loc['expect_No', 'No'])}\")\n",
    "print(f\"Precision: {cnt.loc['expect_No', 'No'] / (cnt.loc['expect_No', 'No'] + cnt.loc['expect_Yes', 'No'])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function-level recall & precision\n",
    "- Recall: Localize unsound unsafe callees\n",
    "- Precision: The confidence of unsound prediction of unsafe callees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_result(now, new): \n",
    "    if now == \"No\":\n",
    "        return now\n",
    "    if now == \"Yes\":\n",
    "        return new\n",
    "    if new == \"No\":\n",
    "        return \"No\"\n",
    "    return \"unknown\"\n",
    "\n",
    "cnt = pd.DataFrame(np.zeros((2, 3)), columns=[\"No\", \"Yes\", \"unknown\"], index=[\"expect_No\", \"expect_Yes\"])\n",
    "unsafe_call_num = 0\n",
    "unsound_cnt = 0\n",
    "for r in pred_list:\n",
    "    sample_label = r[\"sample_label\"]\n",
    "    if r[\"result\"] != \"unsound\":\n",
    "        continue\n",
    "    unsound_cnt += 1\n",
    "    if sample_label not in gt_dict:\n",
    "        print(f\"Sample {sample_label} not found in ground truth\")\n",
    "        continue\n",
    "    unsafe_call_num += len(sample_dict[sample_label].unsafe_callees)\n",
    "    con_gt = gt_dict[sample_label]\n",
    "    unsafe_callees = dict()\n",
    "    for c_idx, c in enumerate(con_gt):\n",
    "        unsafe_callees[c[\"fn_name\"]] = {\"expect\": \"Yes\", \"pred\": \"Yes\"}\n",
    "    for c_idx, c in enumerate(con_gt):\n",
    "        expect = c[\"expect\"]\n",
    "        if expect == \"Hallucinated\":\n",
    "            if skip_hallucated:\n",
    "                continue\n",
    "            else:\n",
    "                expect = \"Yes\"\n",
    "        unsafe_callees[c[\"fn_name\"]][\"expect\"] = merge_result(unsafe_callees[c[\"fn_name\"]][\"expect\"], expect)\n",
    "        unsafe_callees[c[\"fn_name\"]][\"pred\"] = merge_result(unsafe_callees[c[\"fn_name\"]][\"pred\"], r[\"response\"][c_idx][\"result\"])\n",
    "    for f in unsafe_callees.values():\n",
    "        cnt.loc[f\"expect_{f['expect']}\", f[\"pred\"]] += 1\n",
    "\n",
    "print(f\"Unsafe call number: {unsafe_call_num}\")\n",
    "print(cnt)\n",
    "print(f\"Unsound: {unsound_cnt}\")\n",
    "print(f\"Recall: {cnt.loc['expect_No', 'No'] / (cnt.loc['expect_No', 'Yes'] + cnt.loc['expect_No', 'No'])}\")\n",
    "print(f\"Precision: {cnt.loc['expect_No', 'No'] / (cnt.loc['expect_No', 'No'] + cnt.loc['expect_Yes', 'No'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/huanli/programs/miniconda3/envs/unsafe/lib/python3.10/site-packages/tree_sitter/__init__.py:36: FutureWarning: Language(path, name) is deprecated. Use Language(ptr, name) instead.\n",
      "  warn(\"{} is deprecated. Use {} instead.\".format(old, new), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from utils.sample_utils import Sample\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Performance between Contract Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_prompt = \"decompose_with_self_check\"\n",
    "prompt = \"Safe4U_plus\"\n",
    "model = \"Qwen2-7B-Instruct\"\n",
    "contract_types = [\n",
    "    \"Allocated\",\n",
    "    \"Aligned\",\n",
    "    \"Dereferencable\",\n",
    "    \"Initialized\",\n",
    "    \"Not Null\",\n",
    "    \"Overflow\",\n",
    "    \"Type Constraint\",\n",
    "    \"Numeric Relation\",\n",
    "    \"Unreachable\",\n",
    "    \"Thread Safety\",\n",
    "    \"Locked\",\n",
    "    \"Lifetime Coverage\",\n",
    "    \"End of Use\",\n",
    "    \"Exclusive\",\n",
    "    \"Workflow\",\n",
    "    \"Environment\",\n",
    "    \"Unknown\",\n",
    "]\n",
    "def get_type(type_str: str):\n",
    "    for key in contract_types:\n",
    "        if key in type_str:\n",
    "            return key\n",
    "    return \"Unknown\"\n",
    "\n",
    "sound_samples = json.load(open(f\"data/samples/{model}/{split_prompt}/risky_fine_grained.json\"))\n",
    "unsound_samples = json.load(open(\"data/samples/filtered_unsafe_Qwen_GT.json\"))\n",
    "sound_results = json.load(open(f\"result/{model}/{prompt}/risky_result.json\"))\n",
    "unsound_results = json.load(open(f\"result/{model}/{prompt}/filtered_unsafe_result.json\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "constraint_dict = dict()\n",
    "sample_dict = dict()\n",
    "\n",
    "for sample in sound_samples:\n",
    "    constraint_dict[sample[\"sample_label\"]] = sample[\"constraints\"]\n",
    "    sample_dict[sample[\"sample_label\"]] = Sample(sample)\n",
    "\n",
    "for sample in unsound_samples:\n",
    "    constraint_dict[sample[\"sample_label\"]] = sample[\"constraints\"]\n",
    "    sample_dict[sample[\"sample_label\"]] = Sample(sample)\n",
    "\n",
    "df_map = {t: pd.DataFrame(np.zeros((2, 3)), columns=[\"No\", \"Yes\", \"unknown\"], index=[\"expect_No\", \"expect_Yes\"]) for t in contract_types}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tranverse sound samples\n",
    "for r in sound_results:\n",
    "    sample_label = r[\"sample_label\"]\n",
    "    gt = constraint_dict[sample_label]\n",
    "    for c_idx, c in enumerate(r[\"response\"]):\n",
    "        c_type = get_type(gt[c_idx][\"type\"])\n",
    "        df_map[c_type].at[\"expect_Yes\", c[\"result\"]] += 1\n",
    "\n",
    "# traverse unsound samples\n",
    "for r in unsound_results:\n",
    "    sample_label = r[\"sample_label\"]\n",
    "    if r[\"result\"] != \"unsound\":\n",
    "        continue\n",
    "    gt = constraint_dict[sample_label]\n",
    "    for c_idx, c in enumerate(r[\"response\"]):\n",
    "        expect = gt[c_idx][\"expect\"]\n",
    "        # FIXME: skip hallucinated constraints or not\n",
    "        if expect == \"Hallucinated\":\n",
    "            continue\n",
    "        c_type = get_type(gt[c_idx][\"type\"])\n",
    "        df_map[c_type].at[f\"expect_{expect}\", c[\"result\"]] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 Types with the lowest precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision = dict()\n",
    "for c_type in contract_types:\n",
    "    df_type = df_map[c_type]\n",
    "    total = df_type.at[\"expect_No\", \"No\"] + df_type.at[\"expect_Yes\", \"No\"]\n",
    "    expect_no = df_type.at[\"expect_No\", \"Yes\"] + df_type.at[\"expect_No\", \"No\"]\n",
    "    precision[c_type] = df_type.at[\"expect_No\", \"No\"] / total if total > 0 and expect_no > 0 else 2\n",
    "\n",
    "del precision[\"Unknown\"]\n",
    "precision = sorted(precision.items(), key=lambda x: x[1], reverse=False)\n",
    "\n",
    "top_10_error_rate = precision[:8]\n",
    "labels, rates = zip(*top_10_error_rate)\n",
    "fig, ax = plt.subplots(figsize=(4, 3), dpi=300)\n",
    "\n",
    "# Bar style enhancements\n",
    "ax.bar(labels, rates, color='lightskyblue', edgecolor='darkblue', linewidth=0.6, width=0.5)\n",
    "\n",
    "# Title and Labels styling\n",
    "name = \"Qwen\" if model == \"Qwen2-7B-Instruct\" else model\n",
    "# ax.set_title(f'Top 10 Error-Prone Contract Types ({name})', fontsize=14, fontweight='bold')\n",
    "ax.set_xlabel('Contract Types', fontsize=10, fontweight='bold')\n",
    "ax.set_ylabel('Precision', fontsize=10, fontweight='bold')\n",
    "\n",
    "# Ticks styling\n",
    "plt.xticks(rotation=30, ha='right', fontsize=6)\n",
    "# plt.xticks(rotation=90, ha='center', fontsize=8)\n",
    "plt.yticks(fontsize=8)\n",
    "\n",
    "# Adding gridlines for better readability\n",
    "ax.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "plt.tight_layout()  # Adjust layout for better spacing\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 Types with the lowest recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rate = dict()\n",
    "for c_type in contract_types:\n",
    "    df_type = df_map[c_type]\n",
    "    total = df_type.at[\"expect_No\", \"Yes\"] + df_type.at[\"expect_No\", \"No\"]\n",
    "    rate[c_type] = df_type.at[\"expect_No\", \"No\"] / total if total > 0 else 2\n",
    "\n",
    "del rate[\"Unknown\"]\n",
    "rate = sorted(rate.items(), key=lambda x: x[1], reverse=False)\n",
    "\n",
    "top_10_error_rate = rate[:8]\n",
    "labels, rates = zip(*top_10_error_rate)\n",
    "fig, ax = plt.subplots(figsize=(4, 3), dpi=300)\n",
    "\n",
    "# Bar style enhancements\n",
    "ax.bar(labels, rates, color='lightskyblue', edgecolor='darkblue', linewidth=0.6, width=0.5)\n",
    "\n",
    "# Title and Labels styling\n",
    "name = \"Qwen\" if model == \"Qwen2-7B-Instruct\" else model\n",
    "# ax.set_title(f'Top 10 Error-Prone Contract Types ({name})', fontsize=14, fontweight='bold')\n",
    "ax.set_xlabel('Contract Types', fontsize=10, fontweight='bold')\n",
    "ax.set_ylabel('Recall', fontsize=10, fontweight='bold')\n",
    "\n",
    "# Ticks styling\n",
    "plt.xticks(rotation=30, ha='right', fontsize=6)\n",
    "# plt.xticks(rotation=90, ha='center', fontsize=8)\n",
    "plt.yticks(fontsize=8)\n",
    "\n",
    "# Adding gridlines for better readability\n",
    "ax.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "plt.tight_layout()  # Adjust layout for better spacing\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "unsafe_rust",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
